[BE]: Update mypy to 1.13.0 (#140808)

Update mypy to 1.13.0 . Should hopefully reduce linting time. Has support for orjson cache serialization which should improve mypy cache perf if orjson is installed.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/140808
Approved by: https://github.com/ezyang, https://github.com/malfet

torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py

@@ -105,6 +105,7 @@ def post_localSGD_hook(
     # Run allreduce using `global_group_to_use` in the first `start_localSGD_iter` iterations.
     if state.iter < state.start_localSGD_iter:
         state.maybe_increase_iter(bucket)
+        assert isinstance(global_group_to_use, dist.ProcessGroup)
         return default._allreduce_fut(global_group_to_use, input_tensor)
 
     # If `post_local_gradient_allreduce` is not set,

torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py

@@ -7,6 +7,7 @@ from typing import Dict
 import torch
 import torch.distributed as dist
 from torch.distributed import distributed_c10d
+from torch.utils._typing_utils import not_none
 
 from . import default_hooks as default
 
@@ -398,7 +399,10 @@ def powerSGD_hook(
         >>> ddp_model.register_comm_hook(state, powerSGD_hook)
     """  # noqa: B950
     process_group = state.process_group
-    group_to_use = process_group if process_group is not None else dist.group.WORLD
+    group_to_use = (
+        process_group if process_group is not None else not_none(dist.group.WORLD)
+    )
+    assert isinstance(process_group, dist.ProcessGroup)
     world_size = group_to_use.size()
 
     # The input tensor is a flattened 1D tensor.
@@ -707,7 +711,10 @@ def batched_powerSGD_hook(
         >>> ddp_model.register_comm_hook(state, batched_powerSGD_hook)
     """  # noqa: B950
     process_group = state.process_group
-    group_to_use = process_group if process_group is not None else dist.group.WORLD
+    group_to_use = (
+        process_group if process_group is not None else not_none(dist.group.WORLD)
+    )
+    assert isinstance(group_to_use, dist.ProcessGroup)
     world_size = group_to_use.size()
 
     # The input tensor is a flattened 1D tensor.

torch/distributed/algorithms/model_averaging/averagers.py

@@ -1,11 +1,12 @@
 # mypy: allow-untyped-defs
 import warnings
 from abc import ABC, abstractmethod
-from typing import Dict, Iterable, Union
+from typing import Dict, Iterable, Optional, Union
 
 import torch
 import torch.distributed as dist
 import torch.distributed.algorithms.model_averaging.utils as utils
+from torch.utils._typing_utils import not_none as _not_none
 
 
 __all__ = ["ModelAverager", "PeriodicModelAverager"]
@@ -21,9 +22,9 @@ class ModelAverager(ABC):
                        will be used. (default: ``None``)
     """
 
-    def __init__(self, process_group=None):
+    def __init__(self, process_group: Optional[dist.ProcessGroup] = None):
         self.process_group = (
-            process_group if process_group is not None else dist.group.WORLD
+            process_group if process_group is not None else _not_none(dist.group.WORLD)
         )
         self.step = 0
 
@@ -85,7 +86,9 @@ class PeriodicModelAverager(ModelAverager):
         >>>    averager.average_parameters(model.parameters())
     """
 
-    def __init__(self, period, warmup_steps=0, process_group=None):
+    def __init__(
+        self, period, warmup_steps=0, process_group: Optional[dist.ProcessGroup] = None
+    ):
         super().__init__(process_group)
         if warmup_steps < 0:
             raise ValueError("Arg ``warmup_steps`` must be a non-negative number.")
@@ -120,5 +123,7 @@ class PeriodicModelAverager(ModelAverager):
             self.step >= self.warmup_steps
             and (self.step - self.warmup_steps) % self.period == 0
         ):
-            utils.average_parameters_or_parameter_groups(params, self.process_group)
+            utils.average_parameters_or_parameter_groups(
+                params, _not_none(self.process_group)
+            )
         self.step += 1